ScopeMaster – Release History
Release 1.32 7 December 2018
- New: Suggested test cases tab for each user story, positive and negative functional test cases
- New: Suggested test cases report, positive and negative functional test cases
- Improved: stability and performance
Release 1.31 11 November 2018
- New: ScopeMaster Plugin for Jira Cloud Visit the plugin on the Atlasssian Marketplace
- Improved: additional story improvement suggestions.
- New: Introducing the ScopeMaster Quality Score
- Improved: Portfolio level view of quality
Release 1.21 11 October 2018
- Fixed: Adding a multi-line story via the simple add box.
- Fixed: Removed “so that” warning, potentially misleading.
- Fixed: Default user timezone now set.
- Performance improvements
Release 1.2 August 2018
- Improved navigation for faster and easier story correction
- Quickly revert to any previous version of a user story
- Minor UI improvements and browser compatibility fixes
- Performance and security improvements
- Deprecated the text analysis table
- Improved accuracy of defect reporting, by removing list as an expected function.
Release 1.11 (current), 18 July 2018
- NEW Explorer. This is an additional capability that enables portfolio analysis of users and objects.
- As ScopeMaster analyses requirements it builds up an inventory of the users and objects that are maintained across the software systems of your enterprise. These are now visible with an easy-to-navigate explorer, so you can see an enterprise-wide view of the applications in which a particular user or object is maintained.
- Ideal for:
- Planning and estimating the impact of legislative change on corporate systems.
- Insight into potential technical debt across systems.
- Insight relating to application lifecycle planning.
- Identifying risk areas associated with data security
- Minor UI improvements (easier navigation for grooming user stories)
- Improvements to search results.
Release 1.1, 28 June 2018
- Context specific guidance on improving each story. (learn to improve your stories faster)
- Improved meta information about a set of requirements, including changes over time and size statistics.
Production release 1.01, 1 June 2018
- New simple structured user story input (makes it easier to get it right first time!)
- Jira Integration (import stories directly from your Jira repository)
Production release 1.0, 13 May 2018
- Minor bug fixes
- Improved reports and navigation
Pre-Production release 0.91 4 May 2018
- Easier to find and fix consistency errors – improved users and objects display
- Improved help pages
- Improved access to previous versions
- Corrected interpretation of the word “status”
Pre-Production release 0.89 29 March 2018
- New ambiguities reporting (thanks to Richard Bender)
- minor bug fixes
Pre-Production release 0.88, 25 March 2018
- Improved full screen display and responsive menus
- Improved defects report
- Easy navigation back to recently visited user stories
Pre-Production release 0.87, 17 March 2018
- NEW Sortable, searchable table, ideal for story grooming
- Improved text analysis accuracy
- Simplified defects report
- Improved performance for very large projects
Pre-Production release 0.85, 6 March 2018
- NEW Users can share work with others in their organization.
- NEW Share work at the application level: owners can assign read or edit access to others in their organisation.
- NEW Requirement text within square brackets will be ignored from sizing analysis.
- NEW Requirement export/download as csv.
- Improved IFPUG Function Point estimates, with function-by-function details report.
- Improved text analysis accuracy.
- Improved UI and bug fixes.
- Improved searching
- Improved application performance.
- Improvements to application data security.
- Major improvements to server(s) security.
Beta, 14 December 2017
- analyses the text to describe software requirements
- Interprets user story terminology and common active phrases
- identifies candidate users and objects from the entire body of requirements text
- detects potentially defective requirements – ambiguous
- detects potentially defective requirements – omissions
- detects potentially defective requirements – duplicates
- Identifies functional data movements
- Identifies data to be maintained
- estimates the functional size of the software – in Cosmic Function Points
- provides estimates for: cost to develop, defect potentials, resource requirements and likely schedules.
- Accuracy of functional sizing is currently around 70-80% (Nb. manual accuracy can vary by up to 10%)
- Import by text list or csv
- Ability to take a snapshot track the size progression.
- Portfolio view
- Import 2 column spreadsheet
- Basic text analysis engine
- Initial CFP structure